Presentation of water-entry impact load for TMA during media-cross procedure based on GRNN

Abstract

The investigation on the water-entry impact load of the trans-medium aircraft (TMA) during the media-cross procedure was presented in this paper. The generalized regression neural network (GRNN) is adopted to described the characteristics of the water-entry impact load which is performed by the acceleration variable. In this paper, the train data of the water-entry impact load with the velocity 0, 2m/s, 4m/s, 6m/s, 8m/s, the angle 90°, 80°, 70°, 60°, 50°, the attitude 90°, 80°, 70°, 60°, 50° are generated by the finite element method based on the coupled Eulerian-Lagrangian (CEL) algorithm. The results show that the GRNN has a good performance on approximating the impact load of the TMA with the root mean square error (RMSE) 19.005. The deep learning algorithm for characterizing water-entry impact load can supply a good reference to the structural load evaluation of the TMA.